Enriched Performance Measure Approach for Reliability-Based Design Optimization.

TLDR

The study proposes an enriched performance measure approach for reliability‑based design optimization aimed at substantially improving computational efficiency in large‑scale applications. The approach launches optimization from a deterministic optimum, employs an enhanced hybrid‑mean‑value method, performs efficient probabilistic feasibility checks, and uses a fast reliability analysis for constraints near the design point, thereby handling nonlinear and nonmonotonic constraints. Deterministic initialization and the hybrid‑mean‑value technique reduce iterations and feasibility‑check costs, and numerical examples confirm that the method achieves greater efficiency and stability than existing reliability‑based design optimization approaches.

Abstract

An enriched performance measure approach is presented for reliability-based design optimization to substantially improve computational efficiency when applied to large-scale applications. In the enriched performance measure approach, four improvements are made over the original performance measure approach: as a way to launch reliability-based design optimization at a deterministic optimum design, as a new enhanced hybrid-mean value method, as an efficient probabilistic feasibility check, and as a fast reliability analysis under the condition of design closeness. It is found that deterministic design optimization helps improve numerical efficiency by reducing some reliability-based design optimization iterations. In reliability-based design optimization, a computational burden on the feasibility check of constraints can be significantly reduced by using a mean value first-order method and by carrying out the refined reliability analysis using the enhanced hybrid-mean value method for e-active and violated constraints. The enhanced hybrid-mean value method is developed to handle nonlinear and/or nonmonotonic constraints in reliability analysis. The fast reliability analysis method is proposed to efficiently evaluate probabilistic constraints under the condition of design closeness. Moreover, two numerical examples are provided to compare the enriched performance measure approach to existing reliability-based design optimization methods from a numerical efficiency and stability point of view.